


'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase__          = logging.get_logger(__name__)

UpperCamelCase__          = {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'trajectory_transformer'
      lowerCAmelCase__							=	['past_key_values']
      lowerCAmelCase__							=	{
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(   self							:       List[str]		,		_A							:       Any=100		,		_A							:       int=5		,		_A							:       Any=1		,		_A							:       int=1		,		_A							:       Dict=249		,		_A							:       Dict=6		,		_A							:       List[str]=17		,		_A							:       int=25		,		_A							:       Tuple=4		,		_A							:       int=4		,		_A							:       Any=128		,		_A							:       Tuple=0.1		,		_A							:       Optional[int]=0.1		,		_A							:       Any=0.1		,		_A							:       List[str]=0.0_0_0_6		,		_A							:       Union[str, Any]=512		,		_A							:       Tuple=0.0_2		,		_A							:       str=1e-12		,		_A							:       List[str]=1		,		_A							:       List[Any]=True		,		_A							:       List[Any]=1		,		_A							:       Union[str, Any]=50_256		,		_A							:       int=50_256		,		**_A							:       Any		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							vocab_size
          UpperCAmelCase__     :						List[str]						=							action_weight
          UpperCAmelCase__     :						Optional[int]						=							reward_weight
          UpperCAmelCase__     :						str						=							value_weight
          UpperCAmelCase__     :						Dict						=							max_position_embeddings
          UpperCAmelCase__     :						Optional[Any]						=							block_size
          UpperCAmelCase__     :						Dict						=							action_dim
          UpperCAmelCase__     :						Optional[int]						=							observation_dim
          UpperCAmelCase__     :						Dict						=							transition_dim
          UpperCAmelCase__     :						Dict						=							learning_rate
          UpperCAmelCase__     :						List[str]						=							n_layer
          UpperCAmelCase__     :						Optional[int]						=							n_head
          UpperCAmelCase__     :						str						=							n_embd
          UpperCAmelCase__     :						int						=							embd_pdrop
          UpperCAmelCase__     :						List[Any]						=							attn_pdrop
          UpperCAmelCase__     :						Any						=							resid_pdrop
          UpperCAmelCase__     :						str						=							initializer_range
          UpperCAmelCase__     :						Optional[int]						=							layer_norm_eps
          UpperCAmelCase__     :						List[str]						=							kaiming_initializer_range
          UpperCAmelCase__     :						Union[str, Any]						=							use_cache
          super().__init__(pad_token_id=_A		,		bos_token_id=_A		,		eos_token_id=_A		,		**_A					)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




